Succinct Trit-array Trie for Scalable Trajectory Similarity Search

Massive datasets of spatial trajectories representing the mobility of a diversity of moving objects are ubiquitous in research and industry. Similarity search of a large collection of trajectories is indispensable for turning these datasets into knowledge. Current methods for similarity search of trajectories are inefficient in terms of search time and memory when applied to massive datasets. In this paper, we address this problem by presenting a scalable similarity search for Fr\'echet distance on trajectories, which we call trajectory-indexing succinct trit-array trie (tSTAT). tSTAT achieves time and memory efficiency by leveraging locality sensitive hashing (LSH) for Fr\'echet distance and a trie data structure. We also present two novel techniques of node reduction and a space-efficient representation for tries, which enable to dramatically enhance a memory efficiency of tries. We experimentally test tSTAT on its ability to retrieve similar trajectories for a query from large collections of trajectories and show that tSTAT performs superiorly with respect to search time and memory efficiency.